Social platforms (e.g., Twitter) and other media-sharing applications (e.g., Instagram) are popular for sharing activities, thoughts, opinions, and images. Geo-tagging of social media messages and images (e.g., associating a physical location or venue with a tweet or a particular image) enables applications to personalize a user's experience based on location information. However, due to privacy concerns, only a small percentage of users choose to publicize their location when they post social media messages or when they take a photo at a particular business venue, and others reveal the locations of their messages/photos only occasionally. Because only a small proportion of images are explicitly geotagged to a location, determining a business venue at which an image was taken (e.g., by identifying concepts in the image) can be useful.
Conventional implementations for identifying geographic locations corresponding to images can be roughly categorized into two groups based on the techniques used for geo-locating: (1) use of coarse-grain locations; and (2) comparison of an image with database images. Some applications attempt to infer coarse-grain locations and provide no indication as to a specific business venue at which an image was taken. Other applications attempt to rely only on database images to match an image with a particular location. These other applications do not take into account textual-based data (e.g., reviews about business venues) and, these other applications fail to work when no database images are available for a particular location. Furthermore, yet other applications rely on low-level visual patterns and do not attempt to recognize visually significant concepts in images and, thus, these yet other applications often cannot differentiate between general consumer images that do not have distinct low-level visual patterns.
Associating an image with a specific business venue at which the image was taken can facilitate better understanding of an image's (or a user associated with the image) geographic context, which can enable better inference of a geographic intent in search queries, more appropriate placement of advertisements, and display of information about events, points of interest, and people in the geographic vicinity of the user. Therefore, there is a need for implementations that address the deficiencies identified above in order to accurately associate images with business venues at which they were taken (e.g., by utilizing text-based reviews and stored images to identify business-aware concepts in images).